Identifications of Patterns of Life Through Analysis of Devices within Monitored Volumes

ABSTRACT

This document presents a system to identify patterns of life assocated with the users of radio frequency emitting devices radiating within a monitored volume. These recurring commonalities in human activity can be derived from collected data and subsequent analytics that provide intelligence about those emitters and associated humans present within the monitored volume. The Freya system provides unique identifiers for detected emitters; insights into the current network relationships between emitters, past and current; human relational networks within the monitored volume; and can identify previous emitter locations prior to detection by the Freya system. These patterns of life provide a foundation for predicting interactions between humans associated with emitters active on the Internet of Things (IoT). This capability provides enahanced security capabilities as required by authorities for sensitive venues and events.

CLAIM TO PRIORITY

This Non-Provisional application claims under 35 U.S.C. §120, thebenefit of the Provisional Application 62/369,265, filed Aug. 1, 2016,Titled “Dignus Application and Facilitated Meetings” which is herebyincorporated by reference in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND

Since 2007, a large proportion of human beings in the world have adoptedthe use of smart phones which they carry on their person as part oftheir daily routine. These smart phones have associated signatures andunique identifiers that, due to the constant proximity to the users whocarry them, have become part of individual's personal identifiableinformation.

Over the last several years, the use of smart phones has been augmentedby networked wearable smart devices, and other portable networkedcommunication devices used by an ever-growing proportion of the world'spopulation. Wearable devices are usually smart watches or similar.Portable devices include laptop computers, tablet computers, Bluetoothenabled cars, etc. These wearable or portable devices are frequentlynetworked with each other, and are increasingly exchanging data withother devices and systems known as the Internet of Things (IoT). Each ofthese devices has a uniquely identifiable signature.

Moreover, as people tend to keep a radio frequency (RF)-emitting devicefor years, the unique identifiers associated with these RF-emittingdevices has effectively become an enduring and consistent uniquelyidentifiable RF signature associated with the person that carries them.These RF signatures can be detected, measured, and used to identify anindividual. This information can be used in environments where securityis a concern.

It is the Freya system which refers to the hardware, software, and userinteraction that comprise the system for monitoring defined volumesthrough detecting and capturing emissions and reflections from theelectromagnetic spectrum, including the Radio Frequency (RF) spectrum,human-visible spectrum, infrared spectrum, ultraviolet spectrum,hyperspectral range, and acoustic pressure waves. The Freya system alsocomprises recording the captured emissions and reflections, creating oneor more databases composed of the recorded multispectral information,and providing detailed analytics derived from the overlay of RF spectrumand/or video data, and/or acoustic waves from said one or more createddatabases.

Once the Freya system collects and stores this RF information into oneor more defined databases, analytics can be drawn to provide significantintelligence about those emitters and associated humans present withinthe monitored volume. Freya provides unique identifiers for detectedemitters; insights into the current network relationships betweenemitters, past and current; human relational networks within themonitored volume; and previous emitter locations prior to detection bythe Freya system.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain illustrative embodiments illustrating organization and method ofoperation, together with objects and advantages may be best understoodby reference to the detailed description that follows taken inconjunction with the accompanying drawings in which:

FIG. 1 is a flow diagram of the application logic flow for devicelocation and meeting consistent with certain embodiments of the presentinvention.

FIG. 2 is a view of the process flow for correlating video derivedinformation with RF, infrared, and/or visible spectrum data formingmultispectral data creation consistent with certain embodiments of thepresent invention.

FIG. 3 is a view of the process flow for creating unique identifiers forunknown RF and other spectrum emitters in a monitored volume consistentwith certain embodiments of the present invention.

FIG. 4 is a view of the process flow for analyzing all collectedmonitoring and defining patterns of life for devices within a monitoredvolume for all spectra in which devices emit information, and providingmetrics, predictions, and recommendations to a user consistent withcertain embodiments of the present invention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail specific embodiments, with the understanding that the presentdisclosure of such embodiments is to be considered as an example of theprinciples and not intended to limit the invention to the specificembodiments shown and described.

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term “plurality”, as used herein, is defined as two or morethan two. The term “another”, as used herein, is defined as at least asecond or more. The terms “including” and/or “having”, as used herein,are defined as comprising (i.e., open language). The term “coupled”, asused herein, is defined as connected, although not necessarily directly,and not necessarily mechanically.

Reference throughout this document to “one embodiment”, “certainembodiments”, “an embodiment” or similar terms means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the presentinvention. Thus, the appearances of such phrases or in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments without limitation.

Reference throughout this document to the “Freya system” refers to thehardware, software, and user interaction that comprise the system formonitoring defined spatial volumes through detecting and capturingemissions and reflections from the electromagnetic spectrum, includingthe Radio Frequency (RF) spectrum, human-visible spectrum, infraredspectrum, ultraviolet spectrum, hyperspectral range, and acousticpressure waves. The Freya system also comprises recording the capturedemissions and reflections, creating one or more databases composed ofthe recorded multispectral information, and providing detailed analyticsderived from the overlay of RF spectrum and/or video data, and/oracoustic waves from said one or more created databases.

Reference throughout this document to the “acoustic pressure wave”refers to an instance of a surface pressure wave, a sound pressure wave,an acoustic wave, acoustic radiation, or sound within or outside thehuman audible range.

Reference throughout this document to “monitored volume” refers to thethree dimensional space within which the system for monitoring definedenvironments and controlled access environments, such as the Freyasystem, can detect all sensor, video, multimedia, and RF data and energyfrom RF, visible spectrum, infrared spectrum, ultraviolet spectrum,hyperspectral range, and acoustic pressure wave emitters or reflectorswithin a defined three-dimensional volume such as a sphere or otherdelineated volume. A monitored volume may be defined or modifieddynamically. A dynamic monitored volume is a monitored volume where theshape and volume can vary as a function of time. A monitored volume maybe static or mobile in three dimentional space. In a non-limitingexample, a monitored volume sphere has a diameter that is directlyrelated to the energy being broadcast by the various emitters locatedwithin the sphere and the gain of the antennas being used by thecontrolled environment monitoring system.

Reference throughout this document to the “Marco Application Server(MAS)” refers to one or more network capable and network connectedservers configured to support and operate one embodiment of the Freyaapplication and system called Marco.

Reference throughout this document to “pattern of life” refers tocommonalities found in the manner in which human beings act and withwhom they act. Patterns of life are derived from commonalities andcorrelations between collected data sets. These patterns of life provideinformation which assists in predictive analysis of future activity,location, or association.

In a non-limiting example, a pattern of life might be two individualswho work together and eat lunch together every weekday at noon at one ofa defined number of restaurants they frequent. In another non-limitingexample, a pattern of life can define a relationship between twootherwise unrelated individuals by correlation of their patterns oflife, such as traveling through the same airport or same hotels. Inanother non-limiting example, a dynamic monitored volume can provideinformation about patterns of life of other individuals existing evenbriefly within one or more dynamic monitored volumes.

Reference throughout this document to “multispectral data” refers todata emitted from any number of regions of the electromagnetic spectrum.Multispectral data can be captured by electromagnetic sensors, RFreceivers, video receivers, audio receivers, infrared receivers,microwave receivers, and other data capture devices to provide datacaptured to the Freya system from a plurality of spectra represented bytwo or more of the data capture devices.

Reference throughout this document to “electromagnetic spectrum data”refers to data derived from one or more regions of the entire range andscope of frequencies of electromagnetic radiation and their respective,associated photon wavelengths. Electromagnetic spectrum data can becaptured by electromagnetic sensors, RF receivers, video receivers,audio receivers, infrared receivers, microwave receivers, and other datacapture devices to provide data captured to the Freya system from aplurality of spectra represented by one or more of the data capturedevices.

Reference throughout this document to a “unique emitter” refers to anemitter that has been found to be one of a kind based on one or morecharacteristics that are not found, and for which no equivalent isfound, in any other entity within the monitored volume.

Reference throughout this document to a “unique identifier” refers to anemitter which alone, or in combination with other traits, creates aunique set of characteristics for an emitter, and permits that emitterto be singly identified from others in a sample group.

Reference throughout this document to “personal identifiable information(PII)” refers to any information that can be used to distinguish ortrace an individual's identity.

Reference throughout this document to “non-repudiation” refers to theassurance that a statement or characteristics cannot be denied. Thisquality of non-repudiation is achieved when sufficient facts or evidenceare amassed that compels a finding to be admitted as truth.

Reference throughout this document to “media access control address” or“MAC address” refers to a unique identifier assigned to a networkinterface associated with a computer. This identifier is present at thedata link layer of the network segment.

Reference throughout this document to “hyperspectral data” refers todata from across the visible and non-visible light spectrum so as toobtain the breadth of the spectrum for every pixel in the image of thescene for the purpose of finding objects, identifying materials ormaterial state, or detecting processes within the image. Hyperspectraldata can be collected using a camera or other hyperspectral imagingsystems to provide data captured to the sensor data collection systemfrom a plurality of spectra represented by two or more sensors withinany number of data capture devices.

A significant proportion of human beings carry on their person a smartphone, a smart watch, or other wireless networked wearable device whichradiates RF, creating signatures and unique identifiers. These uniqueidentifiers, due to the constant proximity to the users who carry them,have become part of individuals' personal identifiable information.These RF signatures can be detected, measured, and used to identify anindividual.

Moreover, other devices such as portable devices, which include laptopcomputers, tablet computers, Bluetooth enabled cars, etc., provideadditional uniquely identifiable signatures which further define the RFsignature associated with a unique individual. Moreover, as people tendto keep an RF-emitting device for years, the unique identifiersassociated with these RF-emitting devices have effectively becomeenduring, consistent, and uniquely identifiable RF signatures associatedwith the person that carries them.

These wearable or portable devices are frequently involved in networkcommunication with each other, and are increasingly exchanging data withother devices and systems known as the Internet of Things (IoT). Thenature of these network exchanges reveal not only the relationshipsbetween unique devices, but provides a view into previous networkassociations among devices. This information into previous networkassociations among devices allows the Freya system, through analyticsand correlation with external databases, to identify previous locationsfor these portable unique identifyers. The information gained from theseanalytics provides a means to capture the travel locations and patternof life for individuals associated with the emitters captured within theFreya monitored volume. In a non-limiting example, this informationprovided by the Freya system can be used in environments where securityis a concern.

These smart phones, networked wearable devices, and associated emitterscreate uniquely identifiable RF patterns associated with individuals.These patterns can be used to identify the presence of a person within amonitored volume, provide analytics on the network communication betweenthe devices within the monitored volume, and therefore derive insightson associations between individuals within the monitored volume.Additionally, these unique patterns can provide forensic data about thedevices present in a monitored volume for a defined period. In such acircumstance, historical data can provide forensic evidence on thepresence of a detected device within the monitored volume during aperiod of interest, and its the individual(s) associated with thedetected device. The derived analytics also provide a historical trailof previous locations for devices detected within the monitored volume,subsequently producing analytics of associations between the detectedemitters and other devices based on network interactions at previouslocations whether within a monitored volume or exterior to a monitoredvolume.

In another embodiment, there is equally an issue that arises when thereis a need to identify one or more individuals within a monitored volume.The need to identify one or more individuals may be associated with aperson who wishes to locate another specific person, or a need to locateindividuals within a monitored volume who correspond to a particularprofile. There exists a need for an application that may anonymouslyprovide identification, assurance, and specific location for individualswho are specified by one or more security authorities as individuals ofinterest or who are associated with a specific profile of individuals ofinterest.

In an alternative embodiment, needs of a security service or a secureenvironment may be met. Since the advent of the Communication Age, humanbeings with certain devices have acquired a distinct RF signature thatcan be associated with their person or entity. Most people keep theircell phone for years and, as a result, this information can be used inenvironments where security is a concern. An additional application mayassist in fulfilling a need by security organizations guarding asensitive venue, defined environment, or event to discover and associatemore information with individuals and devices when they are passingthrough the monitored volume.

In this alternative embodiment, the application may be associated with asurveillance camera that is coupled with an RF sensing antenna array andis used to correlate RF emissions from someone or something in thecamera's field of view. The camera may provide a video feed to a serverwhich will correlate the RF emitter by using one or more directionalantennas, and analyzed by customized algorithms correlating the RFsource to an object or person within a camera's field of view. The Freyasystem may detect emissions using WiFi and Bluetooth protocols, as wellas other RF signals defined as part of one or more additional wirelessnetworking protocols. The Freya system may also detect acoustic pressurewave emissions as a means to augment pattern of life information withinthe monitored volume.

In this embodiment, the RF antenna array will be able to detect uniqueidentifiers from phones emitting queries for embodiments of RFcommunications, for example Bluetooth and WiFi hot spots, otherbroadcast network information, as well as other RF communicationimplementations in its vicinity.

The antenna array and coupled camera system may provide an applicationoperator with information on what RF emitters are correlated with aperson or object within the camera's field of view defined by the videocamera capture of the scene. Movement of the person or object throughthe camera's field of view may also correlate with collected WiFi andBluetooth protocol information, as well as changes in the RF energyreceived by directional antennas in an array, enabling correlationbetween the objects and persons in the camera's field of view. Thiscorrelation may be tagged and identified by the application operator inconjunction with a database composed of this pattern of life informationand the associated individual or entity. The pattern of life informationis collected from within the monitored volume, and may be used touniquely identify detected emitters. The information enables an abilityto identify the presence of unique emitters, and compile a history ofthese detections within monitored volumes. This history becomes thebasis for identifying an emitter's pattern of life. The pattern of lifehistory may be stored in one or more database files associated with theFreya system. These databases may later be utilized as a basis foranalysis and metrics on mobile devices, individuals, and entities invarious locations within the monitored volume. However, a visual imageis not a required data element to detect, identify, and reveal thepattern of life for a device emitting RF, WiFi signals, or otherbroadcast information within any monitored volume.

This system can enable an operator to identify whether a person in thecamera's field of view has one or more phones or other broadcastingdevices, and obtain information on these devices such as a MAC address,other unique identifiers, or grouping of identifiers that create aconcatenated identifier associated with one or more wireless networkingprotocol(s) assigned for each phone or broadcasting device. When thisinformation is recorded by the Freya system, a record is created of thevisual representation from the camera and the RF signatures associatedwith it. These historical records of unique emitters and associatedvideo may be used to enhance security monitoring of a monitored volumeor other defined environments. The Freya system may thus provideforensic information to support and inform investigations followingsecurity events within the Freya system monitored volumes or otherdefined environments.

Additionally, security and law enforcement entities may benefit frommore information being provided about an individual who is a suspect inthe commission of a crime. In a non-limiting example, a suspect orperpetrator of a crime may mask or obscure their physical identity, buthave a mobile device on their person. Capture by the Freya system ofunique identifiers from the mobile device may provide information thatshows that the perpetrator is co-located with the crime site at the dateand time of a criminal action. The unique identifiers may form a chainof interactions with previously encountered RF and/or WiFi locations,permitting a retroactive trace back through locations with which themobile device has recorded an interaction. Moreover, the Freya systemand its analytics capability, drawing from its own and externaldatabases, may also provide information as to previous locations of thecaptured mobile devices, providing significant insights into theassociated individual's pattern of life. Additional information may becaptured for later forensic analysis.

In an alternative embodiment, if a security organization is activelyguarding a controlled environment, this organization may benefit frominformation about individuals that is ancillary to visible informationavailable. Utilizing an infrared sensor, the infrared signature mayprovide information such as the temperature of the individual holding amobile device that could indicate the presence of a fever associatedwith a communicable disease carried by that individual. Additionally,the Freya system can identify relationships amongst networked emittersover a span of time. Subsequently, it is possible to correlate betweenthe emitter associated with the individual suspected of having acommunicable disease and associates who arrive at the Freya monitoredvolume such as an airport at a later date. These associates may wellhave been exposed to the communicable disease, yet show no symptoms. TheFreya system may provide the data correlating the sick individual withother asymptomatic communicable disease carriers in a manner thatprovides public health authorities with the time and informationnecessary to forestall disease transmission. Using additional Freyasystem capabilities, it is also possible to derive information onpattern of life and previous travel locations for symptomatic andasymptomatic individuals, thereby providing a means to better understandthe possible source and potential transmition locations for a disease.

In an alternative embodiment, the collected information from variousdevices and subsequent analysis may indicate when the mobile devicechanges hands from one individual to another once both individuals areconfirmed to be within a monitored volume. This is detected byidentifying a change in the associations between the device emissionsand other information from the monitored volume including data from theRF spectrum, visible spectrum, ultraviolet spectrum, infrared spectrum,hyperspectral range, and/or acoustic pressure waves.

In an embodiment, the Freya system may identify the history of theinteraction of a WiFi device with past locations and systems with whichthe WiFi device has interacted. When entering a new location, a WiFienabled device may broadcast an identification transaction message todetermine if the WiFi enabled device is in proximity to a network orWiFi access point. Each WiFi device may broadcast a sequence of queriesto determine if the WiFi device is within broadcast range of apreviously encountered WiFi network access point.

In standard operation, the WiFi enabled device broadcasts a series ofidentification transaction messages in an effort to connect to a networkor WiFi device with which the WiFi enabled device has previouslyinteracted. The advantage of such broadcasts is to optimize connectionand data transfer with any encountered network and/or other WiFi accesspoint. The Freya system may capture the identity and location of everynetwork and WiFi transmitter with which the WiFi enabled device hasinteracted and continues to query to create a file of locations the WiFienabled device has encountered. The Freya system may analyze the file oflocations to create a list of locations the user of the WiFi enableddevice has visited.

The Freya system may also analyze the file locations for a particularWiFi enabled device and cross correlate this list of locations, as wellas a range of time for each location visit, with those locations visitedby other WiFi enabled devices. This cross correlation may permit theFreya system to form connections between WiFi enabled devices that havevisited the same location witin a defined span of time. Additionally,the Freya system may analyze the number of times such cross correlationshave occurred to construct a determination of how strong the associationmay be between two or more WiFi enabled devices.

In an embodiment, the Freya system may also access and analyze publiclyavailable databases to perform additional analysis to create metricsregarding locations and/or correlations between multiple WiFi enableddevices. The Freya system may also determine unique identifiers derivedfrom emitted data associated with WiFi enabled devices to detect andunderstand whether and how WiFi enabled devices change location, areadded to a monitored volume, and how these devices move through aparticular monitored volume and/or move from one monitored volume toanother.

These embodiments may be utilized in one or more non-limiting examplesincluding but not limited to:

-   -   1. Monitored volumes such as those defined for airports, train        stations, or large public events. In a non-limiting example,        airport security personnel obtain information from the Freya        system on people transiting through areas of the airport. When        correlated with additional databases, this provides a real time        alert to security personnel that a unique RF identifier        associated with a known person of interest is present at the        airport. In addition, using directional antennas and filters in        one or more queries associated with this embodiment, security        personnel are alerted to unusual patterns, such as a person with        several cell phone devices, or an operating cell phone inside        checked luggage. Another query filter enables security personnel        to use Freya system directional antennas to provide an alert        about a device being handed to one, or multiple, individuals        during the course of a visit to an airport. The system also        provides forensic information to security personnel after a        security-related event. Such may be the case when an event        occurs, and stored camera and correlated RF information can be        used to obtain identities of those present at or near the event        using law enforcement databases being queried with information        obtained and stored by this embodiment of the system.    -   2. Monitored volumes such as those defined for large        transportation hubs, or large public events. In a non-limiting        example, public health officials with access to analytics from        the Freya system identify an individual arriving from abroad        whose thermal (infrared) signature depicts a high fever. Customs        and Immigration authorities are alerted to the possibility that        this individual may carry a communicable disease, and        precautions are taken. Upon further evaluation of Freya system        analytics derived from RF collection using directional antennas,        it is determined that the sick individual's portable devices        have previous common associations with the mobile devices of        three other passengers arriving on a different flight later that        day from the same point of origin. Although those three        individuals are asymptomatic, the association with the original        passenger afflicted with a fever enable public health        authorities to take appropriate precautions.    -   3. Defined controlled-access environments such as schools,        police stations, and government buildings. Much like the airport        scenario, an alternative embodiment of the system is used in        police vehicles, providing information to law enforcement on the        RF signatures correlated to the picture on a video camera. This        has the potential of being correlated to law enforcement        databases. The system helps identify a known dangerous person in        a stopped vehicle prior to the police officer making an approach        to that vehicle. Also, in the event of a police officer being        incapacitated, the information from the database records created        and analyzed by the system is used after the fact to help        identify the driver of the stopped vehicle, even if the stopped        vehicle's license plate and description cannot be otherwise        correlated to the perpetrator, such as in a stolen car scenario.    -   4. Interacting with both home and retail store security systems        that detect video and RF to provide forensic evidence on the PII        for any intruders to the home or retail location. In the event        of a robbery, law enforcement can be provided with the database        records retrieved from the Freya system server in order to        correlate the video and monitored volume RF information as part        of the evidence package that can be used to find and apprehend        the perpetrator, even if the video does not provide visual clues        to the identity of the perpetrator. In this case, the RF        signature correlated by the system can be run against law        enforcement databases, or become the trigger for subpoenas to        cellular providers in order to identify the perpetrator in a        manner that cannot be done today.    -   5. Banks, jewelry stores, convenience stores, or other merchant        locations. This embodiment of the system is used to create a        local record of those who frequent the store that is Freya        system enabled. The analysis of recorded RF and video        information in the monitored volume that is the retail or        corporate location may provide a merchant with a better        understanding of their customers. The analysis of the recorded        RF and video information may enable the derivation of habits and        shopping preferences for customers who frequent the retail or        corporate locations.    -   6. The Freya system provides an automated means to take        attendance in schools, classrooms, and workplaces. The Freya        system can provide an accounting of employee presence in the        workplace, as well as highlight those individuals who may not be        authorized to be within a monitored volume within a school,        classroom, or workplace.    -   7. The Freya system may be integrated with facial recognition        software to provide for enhanced analytics from surveillance        systems.    -   8. The analytics provided by the Freya system may enable        identifying human relationships and human networks within        groups. The analytics from Freya monitored volumes provide        insight into the network associations common amongst two or more        identified emitters. The larger the number of common past or        current network association between emitters, the greater the        probability that the individuals associated with those emitters        know each other. For example, analytics from the Freya system        would enable authorities to identify members of a criminal gang        who have been identified in separate Freya monitored volumes,        but whose networked device associations provide a strong        correlation among those individuals.

Turning now to FIG. 1, this figure presents a flow diagram of theapplication logic flow for device location and meeting consistent withcertain embodiments of the present invention. This embodiment of theFreya system is of two or more individuals who want to find each otherthrough the use of smart phone devices while in a crowded environment,or by individuals who have never met and cannot recognize eash other.This embodiment of the Freya system is called Marco and it usesmonitored volumes which are both dynamic and mobile.

In an exemplary embodiment, a user may engage with the Marco app throughthe Marco Application Server (MAS) to facilitate an anonymous meeting.The individual or entity requesting the service from the Marco app isentitled “User1” when initiating the Marco app through a mobile device100. The Marco app uses an electromagnetic and an acoustic sensorproviding the process through which the monitored volume is monitored.In this non-limiting embodiment, User1 is interested in the product andwants to begin using the Marco app. Prior to initiating an anonymousmeeting, User1 must first create an account with the MAS. Initiating theaccount may consist of User Registration for account validation viapayment details, EULA Acceptance (Required), and Assignment of UniqueAccount Name, authentication information, and portal page access.

In this non-limiting embodiment, the account is created by the user onthe registration page by entering the following data fields: User Name(shall be a unique identifier, not an email address), Phone Number,Payment Information, Full Name, Address, Credit Card Information, EmailAddress, and Password. When the account is successfully associated withthe mobile device in the Marco database, the application will check todetermine if User1 has available service credits to establish andcoordinate a meeting 104. If User1 does not have any available servicecredits, User1 may be provided the opportunity to purchase such servicecredits 108. The Marco application may update the user database tocreate an account balance of available meeting permission credentialsassociated with the account, allowing the registered user (for instance,User1) to schedule a meeting. In a non-limiting example, aftercompleting account creation and setup, User1 wants to set up a meeting.To accomplish this task, User1 opens the iPhone or Android Marco app,and clicks on the screen button to set up a new meeting. User1 receivesfeedback from the Marco app that a positive meeting permissioncredential balance is available for this meeting. User1 entersinformation into the Marco app regarding the meeting. The entry fieldwill require the time for the meeting, date for the meeting, and thetime zone in which User1 will be located at the time of the meeting. Theother individuals or entities involved in the meeting will not beidentified to the Marco app, and will not be part of the metadataassociated with the meeting permission credential now tagged with timeand date for the meeting. The meeting permission credential, which maybe a token, cipher, certificate, or any other data construct that may beused as a credential, may be a custom data file type that is held insidethe Marco app.

In a non-limiting embodiment, once the Marco application has determinedthat User1 has available service credits, the application prompts User1to enter meeting information for the meeting to be facilitated 112. TheMarco application transmits the meeting information entered by User1 toone or more calendar applications, such as by way of example and not oflimitation, Google Calendar, to which User1 has indicated access 116.The Marco application may then transmit the meeting permission in theform of a meeting permit, which enables additional users and entitiesspecified by User1 during the setup and establishment of the specificmeeting to access the meeting information 120. At the specified date forthe meeting all users and/or entities who are participating in thespecified meeting transmit their meeting permit to the Marcoapplication. Upon receipt of each meeting permit, the Marco applicationtransmits detailed meeting information to each user or entity from whicha meeting permit was received 124. Each user and/or entity may choose toaccept or decline the physical meeting 128. If the user or entitydeclines the meeting, the Marco application dismisses the meeting fromthe database and the Marco application instance terminates 180.

If the user accepts the meeting, the Marco application is operative toupdate the database as to the date and time of the meeting. At a time 20minutes prior to the pre-set meeting time, the Marco application beginsguiding all parties to the meeting by establishing their individualgeographic location 132. The Marco application coordinates geographiclocation and timing for all parties to achieve a meeting 136. The Marcoapplication is also active to transmit updates regarding location andtiming to all participates in the meeting 140. The Marco applicationcontinues guiding and updating until at least two participants in themeeting are within 50 meters of one another. At that time, the Marcoapplication may transmit the position and an arrow indicating a vectorwhich, if traversed, will bring the participants closer into physicalproximity, where this vector arrow indicates a bearing from oneparticipant to the User1 participant that initiated and coordinated themeeting 144.

In this embodiment, the Marco application may continue providingguidance updates until at least one participant is within 5 meters ofUser1 148. The Marco application may then determine distance between themobile device associated with User1 and any other mobile deviceassociated with any participant within 5 meters of User1 and bisect thedistance in meters. The Marco application may then send bisecteddistance calculation to both devices now displaying direction anddistance to move through space.

In this embodiment, when the calculated distance is 5 meters or less,User1 is instructed through the display on their mobile device to“Please wait while other party is guided to your location” 152. TheMarco application may then refresh direction and distance calculationand instruct a participant to proceed, and then provide a similarrefresh and instruction for each additional participant or entity thatis verified for the meeting.

When both User 1 and User 2 are within 5 meters of each other, the Marcoapplication may use non-human audible sounds, or exerted acousticpressure waves, to provide additional locational information to theMarco application being used by User 1 and User 2.

The Marco application may send a “Greetings” message to each mobiledevice when the mobile device associated with User1 and each mobiledevice associated with an additional user or entity are calculated to bewithin 1 meter of one another.

In another embodiment, a system is defined for bringing two or moreindividuals together to co-location, each associated with a mobiledevice or smart phone. Marco creates dynamic monitored volumes for eachindividual's device, and brings said dynamic monitored volumes tocoincidence in time and space. Marco does this by facilitatingWiFi/Bluetooth proximity-based guidance. The Marco Application mayinitiate a WiFi/Bluetooth Proximity scanning algorithm. This algorithmbegins with the Marco app building a data structure multi-dimensionalarray. This multi-dimensional array will contain elements for temporaryrecording, including “Bluetooth device address” (received from MAS),“WiFi device address” (received from MAS), signal strength of WiFidevice address, the signal strength of Bluetooth device address, and atimestamp. App1 and App2 will populate elements for “Bluetooth deviceaddress” and “WiFi device address” with corresponding party's Bluetoothand WiFi hardware MAC address from the mobile device associated with thecorresponding party. The mobile WiFi interface will scan nearby accesspoints and compare WiFi hardware device beacons from nearby clientdevices to the mobile WiFi device address provided by MAS and store thisinformation in the multi-dimensional array.

Upon detection of a corresponding WiFi signal by App1 or App2, the Apptoken will update the WiFi device address signal strength field with thereceived signal strength in db. The Bluetooth implementation on themobile device will then scan for Bluetooth hardware device beacons fromnearby client devices for Bluetooth device address provided by MAS andstored in the multi-dimensional array. Signal strength values will besent to MAS server in telemetry updates and MAS will calculate whetherthe distance between User1 and any other device scheduled to meet withUser1 is increasing or decreasing by db strength increases or decreasesfor “target location verified”. At this point the Marco application mayuse non-human audible sounds, or exerted acoustic pressure waves, toprovide additional locational information to the Marco App1 and App2.

The MAS will send instruction to both Marco apps installed on thedevices associated with User1 and any other device that the “Other partyis within range”. At this point, the meeting will be preliminarilyassumed to have been facilitated and the Marco application meetingfunction of the Freya system will terminate 180.

Turning now to FIG. 2, this figure presents a view of the process flowfor correlating system derived information with RF, visible spectrum,infrared spectrum, ultraviolet spectrum, and/or hyperspectral range dataforming multispectral data creation consistent with certain embodimentsof the invention. In an embodiment, the Freya system may detect WiFi,Bluetooth, or other Radio Frequency (RF) networking protocols; capturevisible spectrum, infrared spectrum, ultraviolet spectrum, and/orhyperspectral range video; and provide detailed analytics derived fromthe overlay of these RF spectrum signatures and collected video data200. The Freya system can also perform RF-detection and data analyticsfunctions in the absence of visible spectrum and/or non-visible spectrumcamera(s).

In an embodiment, the Freya system may utilize the results of theRF-detection function to determine the location of all RF emittingdevices that are within the detection range of a monitored volume. Uponinvestigation of the located RF emitting devices, the Freya system wouldhave the ability to correlate any derived representation of people inthe monitored volumes with the collected RF-defined unique identities.

In a non-limiting embodiment, the Freya system may perform, but is notlimited to, data capture from a live video feed from one or morecameras, depending on the volume that requires monitoring though visiblespectrum or non-visible spectrum video. The live video feed, whether inthe visible spectrum, infrared spectrum, ultraviolet spectrum, and/orhyperspectral range, may be analyzed to determine positions within themonitored volume that are co-located with RF broadcast data emittersdetected using directional antennas. The Freya system may analyze thevideo feed from one or more cameras to isolate video of all detectedRF-emitting device locations.

A WiFi receiver may pick up WiFi systems emitting as part of theprotocol's network connectivity requirements and within the signalpropagation limits of this protocol. A Bluetooth (protocol V1 throughV3.x) receiver may pick up all Bluetooth systems emitting as part of theprotocol's network connectivity requirements and within the signalpropagation limits of this protocol. The Bluetooth (protocol V4.x,Bluetooth Low Energy (BLE)) receiver may pick up all Bluetooth v4.x/BLEsystems emitting as part of the protocol's network connectivityrequirements and within the signal propagation limits of this protocol.The Freya system is capable of capturing current and emerging RFnetworking protocols performing similar functions as Bluetooth and WiFiused in mobile devices and Internet of Things equipment.

The Freya system can define associations between detected RF emittersand objects within the Freya camera feed when both sensors aresurveilling the same monitored volume. This capability visually depictsto an operator the Freya camera feed with overlayed RF emitterinformation associated with objects within the displayed picture.

A Freya system software module may create a database of the camera feedand detected RF emitting device data. The database records numerous datasets that may include the times of capture for WiFi station MACaddresses, the times of capture for WiFi access points MAC addresses,the times of capture for Bluetooth devices' MAC addresses, power levelsassociated with these data sets, the number of recorded beacons, andadditional detected information that is assigned to individual datafields. The data stored within the Freya system database may be analyzedto provide metrics for each RF broadcast device within a particularmonitored volume. The Freya system may also format the stored datarecord for display in a user interface. Upon completion of theformatting step, the Freya system may display the camera imagery withinthe monitored volume for each RF-emitting device, co-locating the videoand/or infrared imagery with the physical location of each RF-emittingdevice, and display this location within the user interface display toone or more users.

In an embodiment, the Freya system may provide analytic data with regardto devices emitting RF data as such devices transit between variousFreya monitored volumes. This, in conjunction with analytics on uniqueidentifiers, provides analytics on personnel traffic flow betweenvarious Freya monitored volumes. The analysis module may correlatedetected RF emissions with a visual identity within any of the visiblespectrum, infrared spectrum, ultraviolet spectrum, and/or hyperspectralrange fields of view to identify device owners within a monitoredvolume. This process may also identify individuals that lack an RFsignature as this is becoming far less common, and can be attributed asa uniquely identifying characteristic. Information in the database canbe queried to identify numerous as yet-undefined means to createpatterns and relationships between previously seemingly unrelatedentities within a Freya detection volume.

This capability of the Freya system may protect individual privacy byusing private/public key encryption for transfer and storage ofcollected data. The encrypted data can be made available for use byauthorities when needed for forensic investigations supportingsecurity-related incidents. The Freya system may also detect andidentify wireless networking protocols used by Internet of Thingsdevices.

In this embodiment, the Freya system may begin monitoring a specifiedmonitored volume by capturing any and all visible spectrum, ultraviolet,hyperspectral, or infrared image data from cameras, visiblespectrum-capturing devices, ultraviolet, hyperspectral, and infraredcameras focused on the monitored volume 200. The Freya system may alsodetect all RF emitting devices within the monitored volume and collectthe RF data from each device 202. The Freya system may then usedirectional antennas to determine the physical location, in real time,of each RF emitting device within the monitored volume 204. The Freyasystem may then analyze the visible spectrum, infrared spectrum,ultraviolet spectrum, and/or hyperspectral range camera data to isolateany image that may be associated with the physical location of anydesignated RF emitting device 206.

The Freya system may then attach the identified visible spectrum,ultraviolet, hyperspectral, or infrared image data to the RF dataassociated with the RF emitting device to create a record of the visiblespectrum/ultraviolet/hyperspectral/infrared image, and RF data for eachRF emitting device within the monitored volume using collection from oneor more using directional antennas and algorithms 208. The Freya systemmay also attach collected acoustic pressure wave data to the RF dataassociated with the RF emitting device to create a record of thecollected acoustic pressure waves and RF data for each RF emittingdevice within the monitored volume 208.

Once attached, the Freya system may create one or more records forattached data location in the monitored volume 210. These records may bestored in one or more databases 212. Such multimedia capable databasesmay be co-located with the Freya system, may be on serversgeographically distant from the Freya monitored volume, or may belocated within cloud storage. The Freya system may then format thecreated record for display in a user interface on a display associatedwith the Freya system 214 and display the video imagery and co-locatedRF emitting device to a user 216.

Turning now to FIG. 3, this figure presents a view of the process flowfor creating unique identifiers for unknown RF and other spectrumemitters in a monitored volume consistent with embodiments of theinvention. In this embodiment, WiFi and Bluetooth can broadcast datathat can translate to a unique identifier. If the data sets received byFreya do not contain a unique identifier derived from a single dataentry, Freya can use the various data sets broadcast by each emitter todefine a unique identifier for that emitter as shown in Tables 1 to 3below. This is done by concatenating data sets from any combination ofinformation derived from the WiFi protocol, Bluetooth protocol, bothprotocols, or other wireless networking protocols for every detectedemitter to create a primary key that is unique to each emitter. Thisconcatenated primary key can be used to identify previously detectedemitters during future detection opportunities. The number of data setsthat form a concatenated primary key can vary from one emitter toanother, but consists of sufficient data sets such that any two emittersthat are defined through a concatenated primary key cannot be confusedwith each other.

The Freya system can assign a unique identifier to emitters associatedwith human beings. Subsequently, if the need arises to identify anindividual person from unique emitter identifiers, the Freya databasecan be cross-referenced with information from cellular serviceproviders, wireless networking equipment manufacturers, and/or lawenforcement to obtain a person's name that can provide a critical leadto an active investigation.

Data Collected from Bluetooth and WiFi Emitters

Bluetooth Data Fields

TABLE 1 id uuid name status address uap_lap vendor appearance companycompany_type imp_version manufacturer firmware classic_modeclassic_service_uuids classic_channels classic_major_classclassic_minor_class classic_class classic_rssi classic_tx_powerclassic_features classic_features_bitmap le_mode le_service_uuidsle_address_type le_random_address_type le_company_data le_company_uuidle_proximity_uuid le_major_num le_minor_num le_flags le_rssi le_tx_powerle_features le_features_bitmap ibeacon_range created_at updated_atlast_seen

WiFi Station Data Fields

TABLE 2 ID station_mac fseen lseen power num_packets BSSID probed_essids

WiFi Access Point Data Fields

TABLE 3 BSSID fseen lseen channel speed privacy cipher authenticationpower num_beacons ivs lan_IP id_length ESSID priv_key

In a non-limiting embodiment, utilizing the data captured as disclosedin Tables 1-3, the Freya system may create a record of PersonalIdentifiable Information (PII) about an individual that is usuallyassociated with any device or devices that are active within a monitoredvolume defined by the Freya system. The Freya system may use the PII toprovide monitoring and/or forensic information associated with anydevice actively emitting RF data in a monitored volume. In anon-limiting example, a monitored volume may be active in a retailspace, in a public building such as a school or school classroom, anexterior space such as a concert or sports venue, a heavily traffickedarea such as an airport, or other transportation center, or any othervolume that may be defined as a monitored volume. In this non-limitingexample, and not by way of limitation, the Freya system may provide foran automated means for taking attendance in a classroom, discover andlocate lost equipment containing any sort of RF emitter, identifyinghuman interrelationships and human networks within groups, orunderstanding the characteristics of traffic flows in crowds or groupsof people.

In an embodiment, the Freya system may be directed to focus on one ormore monitored volumes defined for the system 300. The Freya system isoperative to collect RF emitting device data 302. Upon collection ofthis data, the Freya system may create a unique Identifier (ID) for eachRF emitting device located within the monitored volume 304. The Freyasystem may cross reference additional database information to locate theidentification of a person that is normally, or usually, associated withthe RF emitting device 306. If the Freya system cannot locate theidentification of the person 308 normally associated with the RFemitting device, the Freya system may create a temporary ID for the RFemitting device 310. The temporary ID may be created by concatenatingthe unique device MAC address with a system permanent address toformulate a unique temporary ID that may be updated at a later time asmore data about the RF emitting device becomes available to the Freyasystem 310.

If the identification of the person normally associated with the RFemitting device is located, this identification is assigned to the RFemitting device by including this personal identification within therecord of the RF emitting device maintained by the Freya system 312.Upon determination of the identification to associate with the RFemitting device, the Freya system is active to monitor and collectpattern of life data for all identified RF emitting devices within amonitored volume 314. The data associated with the RF emitting device,including any person associated with the RF emitting device, may beformatted and displayed to a user of the Freya system 316.

Turning now to FIG. 4, this figure presents a view of the process flowfor analyzing all monitoring and collected pattern of life data fordevices within a monitored volume for all spectra in which devices emitor reflect information and providing metrics, predictions, andrecommendations to a user consistent with embodiments of the invention.In this embodiment, the Freya system collects and stores within one ormore databases all PII data inclusive of any information that may beused to distinguish or derive an individual's identity. When thecollected PII data is of a sufficient amount, the data may achieve alevel of confidence sufficient that it may not be repudiated. The PIIdata may have amassed sufficient facts or evidence that the data setcompels a finding to be admitted as truth, where statements orcharacteristics about the PII cannot be denied.

In an embodiment, the Freya system detects and records deviceinformation as previously described, as well as PII data, and stores allof this information into one or more database files. The Freya system isthen active to analyze all collected and stored data to providepreviously unknown information about the one or more monitored volumesfrom which the data was collected. In this embodiment, data collectedmay include data fields such as those presented in Tables 4-6.

Bluetooth Data Fields

TABLE 4 id uuid name status address uap_lap vendor appearance companycompany_type imp_version manufacturer firmware classic_modeclassic_service_uuids classic_channels classic_major_classclassic_minor_class classic_class classic_rssi classic_tx_powerclassic_features classic_features_bitmap le_mode le_service_uuidsle_address_type le_random_address_type le_company_data le_company_uuidle_proximity_uuid le_major_num le_minor_num le_flags le_rssi le_tx_powerle_features le_features_bitmap ibeacon_range created_at updated_atlast_seen

WiFi Station Data Fields

TABLE 5 ID station_mac fseen lseen power num_packets BSSID probed_essids

WiFi Access Point Data Fields

TABLE 6 BSSID fseen lseen channel speed privacy cipher authenticationpower num_beacons ivs lan_IP id_length ESSID priv_key

Each Freya system outputs its collected data into a relational databasewhich can be searched. These searches address various attributesassociated with collected data fields. The Freya databases useStructured Query Language (SQL) to mine data from relational databases,providing analytics—knowledge gained from mining vast amounts of dataidentifying correlations and associations from previous seeminglyunrelated data.

The analytics derived from a Freya system database can be used forqueries against one or more external databases to gain greater knowledgeabout the emitters within the Freya monitored volume(s). The Freyadatabase can create an SQL Join to combine records from two or moretables in a database, thereby combining fields from two tables by usingvalues common to each. Freya databases can also use the Union operatorto combine data from two tables which have columns with the samedatatype. When a Union is performed, the data from both tables iscollected in a single column having the same datatype.

In an embodiment, the Freya system may provide current and historicaloutput derived from collection of device and PII data from one or moremonitored volumes for use in other database queries. These otherdatabases may include those operated by law enforcement, customs andimmigration, security agencies, and other governmental organizations.Integrating Freya analytics into these external processes providesknowledge that was previously unavailable, and can be used to vastlyexpand knowledge of ‘who’, ‘what’, ‘when’, and ‘where’ about detecteddevices within one or more monitored volumes. Freya analytics can alsoprovide insights into the collected devices' pattern of life—ahistorical record of where the human associated with the device hastravelled to, including specific geolocations.

In an embodiment, the Freya proprietary software may analyze theinformation stored within the one or more databases through an analyticsengine to provide correlated relationships between emitters andindividuals that are present within the area of detection, which iswithin a monitored volume or a portion of an identified monitored volume400. Additionally, the system software may identify relationshipsbetween access points and nodes, which can be present or absent from thefield of detection. The Freya system software may create a relationalmodel between various emitters and devices, identifying correlationsbetween the devices and the people who use these. This enablesidentification of whom may be traveling with whom by correlating whichdevices share common access points to define a relationship betweenindividuals. The Freya system software may create a historical trail ofprevious networked device locations for detected devices usinginformation about previous network access points. This creates anevidentiary trail of where a device has been prior to entry into theFreya-monitored volume.

In an embodiment, the Freya system software may provide analytics toidentify correlations between networked emitters and individuals withinthe database and identify previously unknown contact history prior toentering one or more monitored volumes. In this embodiment, Freya sensorunits and their databases can be shared in a network together, enablingdata mining and a more robust understanding of unique RF-derivedidentities within a larger monitored volume.

The Freya infrared video data can be used to identify unique thermalsignatures for individuals within the camera field of view, enabling theidentification of those who may have a fever as an indicator of sicknessor disease. The analytics engine associated with the Freya system maythen derive unique identifying information about those individuals. Thisis important for transportation hubs where authorities want to minimizethe spread of pathogens to other geographic areas.

In an embodiment, the Freya system can capture camera data in theultraviolet portion of the electromagnetic spectrum. This providescapabilities to correlate detected multispectral data with theultraviolet camera collection. Ultraviolet imagery provides informationnot available in the visible spectrum, such as residual traces ofhazardous chemicals, organic residue, and forensic evidence found ontextile fibers such as that used in suitcases or backpacks. Thisdetected data can then be associated and correlated to collectedmultispectral data with unique RF or networking identifiers. In someinstances, this type of collection can provide authorities with insightsinto hazardous materials/chemicals being brought in suitcases orcarry-on luggage by travelers using mass transportation systems.

In an embodiment, the Freya system can capture hyperspectral imagery.This capability allows for the overlaid collection of data across thevisible and non-visible light spectrum so as to obtain a wide breadth ofthe spectrum for every pixel in the image of the scene. Subsequently,this enables the Freya system with an ability to differentiate betweenorganic and synthetic materials, and some variations in the chemical orphysical composition of objects within the captured camera view. This isvery useful for authorities as a means to identify undesired tracematerials or chemicals on objects brought into a controlled monitoredvolume such as an airport or large public gathering.

In an embodiment, the Freya system can capture acoustic pressure waves.This capability allows for the overlaid collection of data across thehuman audible and non-audible range of acoustic pressure waves so as toobtain identifiers associated with a collected RF emitter. This canenable a capture of a human voice with its unique audible signatures, adistinctive sound such as a telephone custom ringtone, or a squeak in ashoe punctuated by a unique human walking gait. All of these acousticpressure wave signatures can provide additional information associatedwith collected RF data.

The Freya databases are also able to retain historical data that can bequeried against newly collected data, thereby providing analytics onhistorical patterns. This may include the frequency and historicalcorrelations between any two or more emitters; an example of which isthe identification of two unique emitters arriving every Tuesday at noonand departing at 4 PM every week. When these historical analytics arecombined with unique identifiers, the Freya system can provide evidenceapproaching non-repudiation to investigations or inquiries.

In an embodiment, the Freya system may access local Freya databaserecords associated with a monitored volume to review all device and PIIdata available within one or more monitored volumes 402. The Freyasystem may also access database records that are external to the Freyasystem that contain data about the RF emitting device, PII data, or both402. The Freya system is operative to analyze local and externaldatabase records to determine pattern of life for the one or more humanbeings that are normally, or usually, associated with each RF emittingdevice 404. If the human and RF emitting device are not of interest 406,based upon user queries, the Freya system continues to analyzeadditional data for other devices, persons, and monitored volumes.

If an identified person is a person of interest to authorities, againbased upon user queries, the Freya system may access additional databaserecords of other monitored volumes and sources 408. The Freya system isoperative to analyze the data from each monitored volume and/or sourceof data to determine historical data about locations of each RF emittingdevice and to draw associations and correlations between RF emittingdevices and the humans associated with each RF emitting device 410. TheFreya system may then create analytic predictions about the monitoredemitters based upon historical data captured and stored within the Freyasystem 412. The Freya system may then display correlations,associations, predictions and recommendations with regard to each RFemitting device associated with a person of interest for the system user414.

While certain illustrative embodiments have been described, it isevident that many alternatives, modifications, permutations andvariations will become apparent to those skilled in the art in light ofthe foregoing description.

We claim:
 1. A system for detecting and correlating patterns of lifethrough the detection of physical devices within a dynamic monitoredvolume, comprising: a sensor associated with a processor passivelymonitoring data being broadcast from any of a plurality ofelectromagnetic spectrum emitting devices; said processor defining aunique identifier for each of said electromagnetic spectrum dataemitting devices; the processor to analyze received electromagneticspectrum emitted data sets to catalog the various data sets beingbroadcast and the time of capture for said electromagnetic spectrum dataemitting devices within said dynamic monitored volume; storing in adatabase associated with a server one or more created database recordswithin said database and presenting any of said database records to auser upon user request; analyzing the electromagnetic spectrum emitteddata sets, defining a unique emitter identifier, logging the time whenthe data sets were emitted, and identifying the monitored volume orvolumes within which the data sets were emitted; storing the collecteddata on said server, and deriving analytics to define patterns of lifeon commonalities and correlations between collected data sets; anddisplaying said derived patterns of life and analytics associated withthe collected data to a user in a visual representation.
 2. The systemof claim 1, further comprising displaying said created database recordsto a user in a visual representation of the monitored volume.
 3. Thesystem of claim 1, where a visual spectrum data portion of theelectromagnetic spectrum data is captured by one or more of a camera,smartphone camera, or visible spectrum image capture device.
 4. Thesystem of claim 3, where the visual spectrum data captured is associatedwith one or more electromagnetic spectrum data emitting devices.
 5. Thesystem of claim 1, further comprising capturing infrared data by aninfrared camera or other infrared capture device.
 6. The system of claim5, where the infrared data captured is associated with one or moreelectromagnetic spectrum data emitting devices.
 7. The system of claim1, further comprising capturing ultraviolet data by an ultravioletcamera or other ultraviolet capture device.
 8. The system of claim 7,where the ultraviolet data captured is associated with one or moreelectromagnetic spectrum data emitting devices.
 9. The system of claim1, further comprising capturing hyperspectral data by a hyperspectralcamera or other hyperspectral capture device.
 10. The system of claim 9,where the hyperspectral data captured is associated with one or moreelectromagnetic spectrum data emitting devices.
 11. The system of claim1, further comprising capturing acoustic pressure wave data by anacoustic pressure wave microphone or other acoustic pressure wavecapture device.
 12. The system of claim 11, where the acoustic pressurewave data captured is associated with one or more electromagneticspectrum data emitting devices.
 13. The system of claim 1, furthercomprising creating data structures to provide correlations between datasets, metrics, and predictions associated with each electromagneticspectrum data emitting device within a monitored volume.
 14. The systemof claim 13, further comprising determining points of correlationbetween electromagnetic spectrum emitting devices and the individualscarrying or associated with said electromagnetic spectrum data emittingdevices.
 15. The system of claim 1, where the created database recordscomprise text data, electromagnetic spectrum data, visual data, infrareddata, ultraviolet data, hyperspectral data, and any other recorded data,metadata, or data added by a user.
 16. The system of claim 1, whereinformation derived from the collected data of one or more emittersprovide information about the monitored emitter(s)' previous networkassociations with other devices outside of any monitored volume.
 17. Thesystem of claim 1, where knowledge obtained from the system is stored ina database, from which analytics are performed using databasesassociated with the system or external to the system, where a pattern oflife for the individual or group associated with a monitoredelectromagnetic spectrum data emmitter is derived.
 18. The system ofclaim 1, further comprising a processor that provides for the discovery,interpretation, and communication of meaningful patterns in the datarecords stored within the one or more databases, requesting an analysisof the stored data records, and displaying the analysis results toprovide feedback and answers to the requestor.
 19. The system of claim1, where monitored sensors operating within two or more monitoredvolumes are aggregated to create a larger, combined sensor array. 20.The system of claim 1, where two or more databases containingelectromagnetic spectrum data or metadata aggregate data through anetwork connection.
 21. A system for detecting and correlating patternsof life through the detection of physical devices within a dynamicmonitored volume, comprising: a sensor associated with a processorpassively monitoring data being broadcast from any of a plurality ofwireless networking protocol emitting devices; said processor defining aunique identifier for each of said wireless networking protocol dataemitting devices; analyzing received wireless networking protocol datasets to catalog the various data sets being broadcast and the time ofcapture for said wireless networking protocol data emitting deviceswithin said dynamic monitored volume; storing one or more createddatabase records within a database and presenting any of said databaserecords to a user upon user request; analyzing the wireless networkingprotocol emitted data sets, defining a unique emitter identifier,logging the time when the data sets were emitted, and identifying themonitored volume or volumes within which the data sets were emitted;storing the collected data, and deriving analytics to define patterns oflife on commonalities and correlations between collected data sets; anddisplaying said derived patterns of life and analytics associated withthecollected data to a user in a visual representation.
 22. The systemof claim 21, further comprising displaying said created database recordsto a user in a visual representation of the monitored volume.
 23. Thesystem of claim 21, where a visual spectrum data portion of the wirelessnetworking protocol is captured by one or more of a camera, smartphonecamera, or visible spectrum image capture device.
 24. The system ofclaim 23, where the visual spectrum data captured is associated with oneor more wireless networking protocol data emitting devices.
 25. Thesystem of claim 21, further comprising capturing infrared data by aninfrared camera or other infrared capture device.
 26. The system ofclaim 25, where the infrared data captured is associated with one ormore wireless networking protocol data emitting devices.
 27. The systemof claim 21, further comprising capturing ultraviolet data by anultraviolet camera or other ultraviolet capture device.
 28. The systemof claim 27, where the ultraviolet data captured is associated with oneor more wireless networking protocol data emitting devices.
 29. Thesystem of claim 21, further comprising capturing hyperspectral data by ahyperspectral camera or other hyperspectral capture device.
 30. Thesystem of claim 29, where the hyperspectral data captured is associatedwith one or more wireless networking protocol data emitting devices. 31.The system of claim 21, further comprising capturing acoustic pressurewave data by an acoustic pressure wave microphone or other acousticpressure wave capture device.
 32. The system of claim 31, where theacoustic pressure wave data captured is associated with one or morewireless networking protocol data emitting devices.
 33. The system ofclaim 21, further comprising creating data structures to providecorrelations between data sets, metrics, and predictions associated witheach wireless networking protocol data emitting device within amonitored volume.
 34. The system of claim 33, further comprisingdetermining points of correlation between wireless networking protocolemitting devices and the individuals carrying and/or associated withsaid wireless networking protocol data emitting devices.
 35. The systemof claim 21, where the created database records comprise text data,wireless networking protocol data, visual data, infrared data,ultraviolet data, hyperspectral data, and any other recorded data,metadata, or data added by a user.
 36. The system of claim 21, whereinformation derived from the collected data of one or more emittersprovide information about the monitored emitters' previous networkassociations with other devices outside of the any monitored volume. 37.The system of claim 21, where knowledge obtained from the system isstored in a database, from which analytics are performed using databasesassociated with the system or external to the system, where a pattern oflife for the individual or group associated with a monitored wirelessnetworking protocol data emmitter is derived.
 38. The system of claim21, further comprising a processor that provides for the discovery,interpretation, and communication of meaningful patterns in thedatabased data, requesting an analysis of the stored data records, anddisplaying the analysis results to provide feedback and answers to therequestor.
 39. The system of claim 21, where monitored sensors operatingwithin two or more monitored volumes are aggregated to create a larger,combined sensor array.
 40. The system of claim 21, where two or moredatabases containing wireless networking protocol data and/or metadataaggregate said data through a network connection.
 41. The system ofclaim 21, where the wireless networking protocol is the WiFi wirelessnetwork protocol.
 42. The system of claim 21, where the wirelessnetworking protocol is the Bluetooth wireless network protocol.
 43. Asystem for detecting and correlating patterns of life through thedetection of physical devices within a dynamic monitored volume,comprising: a sensor associated with a processor passively monitoringdata being broadcast from any number of acoustic pressure wave emittingdevices; said processor defining a unique identifier for each of saidacoustic pressure wave data emitting devices; analyzing receivedacoustic pressure wave data to catalog the various data sets broadcastand the time of capture for said acoustic pressure wave data emittingdevices within said dynamic monitored volume; storing one or morecreated database records within a database and present any of saiddatabase records to a user upon user request; analyzing the emitted datasets captured from said acoustic pressure wave emitting devices,defining the unique emitter identifier, logging the time when the datasets were emitted, and identifying the monitored volume or volumeswithin which the data sets were emitted; storing the collected data, andderiving analytics to define patterns of life on commonalities andcorrelations between collected data sets; and displaying said derivedpatterns of life and analytics associated with the collected data to auser in a visual representation.
 44. The system of claim 43, furthercomprising displaying said created database records to a user in avisual representation of the monitored volume.
 45. The system of claim43, where a visual spectrum data portion of the acoustic pressure waveis captured by one or more of a camera microphone, smartphonemicrophone, or acoustic pressure wave capture device.
 46. The system ofclaim 45, where the visual spectrum data captured is associated with oneor more acoustic pressure wave data emitting devices.
 47. The system ofclaim 43, further comprising capturing infrared data by an infraredcamera or other infrared capture device.
 48. The system of claim 47,where the infrared data captured is associated with one or more acousticpressure wave data emitting devices.
 49. The system of claim 43, furthercomprising capturing ultraviolet data by an ultraviolet camera or otherultraviolet capture device.
 50. The system of claim 49, where theultraviolet data captured is associated with one or more acousticpressure wave data emitting devices.
 51. The system of claim 43, furthercomprising capturing hyperspectral data by a hyperspectral camera orother hyperspectral capture device.
 52. The system of claim 51, wherethe hyperspectral data captured is associated with one or more acousticpressure wave data emitting devices.
 53. The system of claim 43, furthercomprising creating data structures to provide correlations between datasets, metrics, and predictions associated with each acoustic pressurewave data emitting device within a monitored volume.
 54. The system ofclaim 53, further comprising determining points of correlation betweenacoustic pressure wave emitting devices and the individuals carryingand/or associated with said acoustic pressure wave data emittingdevices.
 55. The system of claim 43, where the created database recordscomprise text data, acoustic pressure wave data, electromagneticspectrum data, visual data, infrared data, ultraviolet data,hyperspectral data, and any other recorded data, metadata, or data addedby a user.
 56. The system of claim 43, where information derived fromthe collected data of one or more emitters provide information about themonitored emitter(s)' previous network associations with other devicesoutside of any monitored volume.
 57. The system of claim 43, whereknowledge obtained from the system is stored in a database, from whichanalytics are performed using databases organic to the system orexternal to the system, where a pattern of life for the individual orgroup associated with a monitored acoustic pressure wave data emmitteris derived.
 58. The system of claim 43, further comprising providing forthe discovery, interpretation, and communication of meaningful patternsin the databased data, and a system to request these analytics, and adisplay system to provide feedback and answers to the requestor.
 59. Thesystem of claim 43, where data obtained from monitored sensors at two ormore monitored volumes is aggregated to create a sensor array.
 60. Thesystem of claim 43, where two or more databases containing acousticpressure wave data and/or metadata aggregate said data through a networkconnection.
 61. The system of claim 1, where an electromagnetic spectrumdata emitting device is any of smart watches, smart phones, portablephones, tablet computers, laptop computers, desktop computers, vehicles,beacons, and/or any other device configured to transmit the RF dataportion from the electromagnetic spectrum data.
 62. The system of claim21, where a wireless network protocol data emitting device is any ofsmart watches, smart phones, portable phones, tablet computers, laptopcomputers, desktop computers, vehicles, beacons, or any other deviceconfigured to communicate using wireless network protocol(s).
 63. Thesystem of claim 43, where an acoustic pressure wave emitting device isany of smart watches, smart phones, portable phones, tablet computers,laptop computers, desktop computers, vehicles, beacons, any other deviceconfigured to broadcast acoustic pressure waves, or acoustic pressurewaves intentionally or unintentionally broadcast by a device within anymedium that propagates said acoustic pressure waves.
 64. A system forbringing two or more individuals, each associated with a mobile deviceor smart phone to co-location by creating dynamic monitored volumes foreach individual, and bringing said dynamic monitored volumes tocoincidence in time and space.
 65. The system of claim 64, furthercomprising two or more monitored volumes.
 66. The system of claim 64,where each dynamic monitored volume is monitored by a sensor that is onor that travels with each of the individuals, and that is associatedwith any of smart watches, smart phones, portable phones, tabletcomputers, laptop computers, desktop computers, vehicles, or any otherdevice configured to receive electromagnetic spectrum data or acousticdata.
 67. The system of claim 1, where the RF sensors in a monitoredvolume are located on a mobile platform such as an automobile, anairborne drone, an unmanned air vehicle, or carried by an individual whois in motion.
 68. The system of claim 21, where the wireless networksensors in a monitored volume are located on a mobile platform such asan automobile, an airborne drone, an unmanned air vehicle, or carried byan individual who is in motion.
 69. The system of claim 1, where thedetected RF data is associated with an object in a camera's field ofview by using one or more directional antennas.
 70. The system of claim21, where the detected wireless communication data is associated with anobject in a camera's field of view by using one or more directionalantennas.
 71. The system of claim 64, each mobile device or smart phoneemiting coded acoustic pressure waves with a predetermined energy levelfrom one or more devices; and receiving the acoustic pressure wave by asecond device associated with each individual, determining distance andgeneral direction, and directing each individuals' motion so as to closethe determined distance and bring said individuals to coincidence intime and space.
 72. The system of claim 64, where each mobile device orsmart phone emiting coded light signals with a predetermined energylevel from one or more devices; and receiving the coded light signals bya second device associated with each individual, determining distanceand general direction, and directing each individuals' motion so as toclose the determined distance and bring said individuals to coincidencein time and space.